Manufacturing Data Maturity Model and First Frontiers

Data and analytics capabilities have leaped forward in recent years. The amount of available data is growing exponentially, more complex algorithms have been developed, and the computational and storage capacity are steadily improving. So, rather than intuition, the new normal is to rely on data to drive digital innovations and business decisions. In fact, data is the “most valuable resource” for organizations including in manufacturing.

Industry 4.0 brings together advanced manufacturing technologies such as artificial intelligence (AI), machine learning (ML), digital twins, augmented reality (AR), and virtual reality (VR) to enable integrated, autonomous, and self-regulating manufacturing systems that operate independently of human intervention. Manufacturing machine/process data can be analyzed by algorithms and used to make critical business and operating decisions in real time that directly affect production output.

As shown in Figure 1, the journey from data collection to digital maturity is a journey in which analysis, context and ideas are added to transform raw data captured from a device or system into information, knowledge and finally actionable wisdom for decision makers.

Figure 1: The stages of the data maturity model on the path to achieving Industry 4.0.

First, data from manufacturing machines/processes is collected, standardized, digitized, and organized as big data. Then, meaning is added and the data is aggregated into knowledge via artificial intelligence. Finally, data is transformed into actionable wisdom captured through shared insights into digital maturity.

Data collection – the first limit

The first and most important frontier for achieving digital maturity in Industry 4.0 is data collection. Data from manufacturing machines/processes is captured via sensors and stored across several key technologies. On the OT side, the data side is stored with controllers, PLCs, gates, and peripherals, and on the IT side with the data center or enterprise cloud. Data storage technology enables long-term storage of digital data captured from advanced sensors. This data-rich environment enables initiatives from the Industrial Internet of Things (IIoT), to big data and simulation, artificial intelligence, adaptive control, and digital twins.

There are some challenges to data collection in manufacturing. Machines and processes in a manufacturing plant are heterogeneous and use different protocols to communicate. Data connectivity is also a major issue due to the outdated nature of factory systems. As a result, IT and OT systems typically do not have an easy way to communicate to enable Industry 4.0 initiatives.

The data broker is one of the key factors enabling to overcome these challenges and to link OT data with IT systems. Using a core standard such as MQTT, a data broker supports the ability to connect to multiple clients that publish data and multiple clients involved to receive data such as enterprise applications. Clients communicating with the broker can abstract the underlying protocol that machines/processes use to communicate. The broker works well in low bandwidth environments with unreliable communication mechanisms due to the basic deployment/subscription method where machines/processes do not need to continue polling for data.

The broker is able to securely communicate data between deployment clients usually on the OT side to clients involved on the IT side. For example, a flow analytics application may want to obtain data from a SCADA system to run its analyzes and publish results in real time. The application will run an MQTT client subscribed to the broker. The SCADA client will publish the data to the broker as it becomes available. As a result, your broker’s co-stream analytics app will automatically get updates without having to poll for the data.

Figure 2 presents a model data structure that shows how a data broker connects multiple devices/processes and applications to enable smooth bi-directional traffic.

Figure 2: Data must be engineered to support multiple data producers and data consumers to link OT to IT.

conclusion

As discussed in this article, when properly harnessed, data is the most valuable asset for many organizations, particularly in manufacturing. To make the most of Industry 4.0 technology, you must turn data into wisdom. A major first step toward this journey is data collection.

There are many challenges to data collection especially when it comes to efficiently transferring data from OT systems to IT systems. Data brokers play a very important role in ensuring that data is available for advanced use cases that allow organizations to take full advantage of Industry 4.0 technology.


About the author

Ravi Subramanian

Ravi Subramanian Manufacturing Solutions Industry Manager at HiveMQ

Ravi Subramanian is a pioneer in product marketing and management with extensive experience delivering high-quality products and services that have generated more than $10 billion in revenue and cost savings for companies such as Motorola, GE, Bosch and Weir. Mr. Subramanian has successfully launched product launches, created brands, created product advertisements and marketing campaigns for global and regional teams.

LinkedIn profile: https://www.linkedin.com/in/ravisubra/

Email ID: Ravi Subramanyan[email protected]>

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